Archive for the ‘Machine Learning’ Category

Machine Learning – Day 4

Day 4 covered methods of automatically identifying clusters in data – and some of the issues that arise using those techniques. Doing this automatic identification is called unsupervised learning, because it doesn’t depend on having a set of labelled data examples to hand. The learning is done purely based on the statistical and probabilistic properties of [...]

Posted on October 22, 2009 at 9:52 pm by Paul Brabban · Permalink · Leave a comment
In: Machine Learning, MSc

Machine Learning – Day 3

Getting through the coursework was a challenge – my computers have never worked so hard. The last section involved performing a computation over a data set that took a few seconds per run to exhaustively search for the optimal settings for two parameters in the computation’s algorithm. Searching over 25 possible settings doesn’t sound like [...]

Posted on October 15, 2009 at 11:29 pm by Paul Brabban · Permalink · Leave a comment
In: Computer Science, Machine Learning, MSc

Machine Learning – Day 2

Day 2 of the Machine Learning MSc module at Manchester saw us learning about Decision Trees and the role that entropy, linear correlation and mutual information can play. It’s all about categorical data (like name, a set of fixed values), whereas last week was about the automated classification of continuous data (like temperature, a smooth [...]

Posted on October 6, 2009 at 11:22 pm by Paul Brabban · Permalink · Leave a comment
In: Machine Learning, matlab, MSc

Machine Learning – Day 1

So I made it in on time for the first day of my Machine Learning course. The train was fantastic, particularly in comparison to the tiny cattle carriage that I ended up last Wednesday. Tip of the day – even on the same routes, not all trains are equal! After the usual stop at the [...]

Posted on October 1, 2009 at 11:31 pm by Paul Brabban · Permalink · Leave a comment
In: Machine Learning, MSc · Tagged with: 

Eigenvalues and Eigenvectors

Yeah, exactly. Eigen-whats? Welcome to the primer material for the Machine Learning module. It  looks pretty mathsy, specifically Linear Algebra (think matrix algebra and Eigen-dooflabs), Differentiation and Integration and some probability and information theory. Yeah, it looks tough. But I’m intrigued, too. Studying the material, I can’t wait to find out how these things actually [...]

Posted on September 17, 2009 at 9:32 pm by Paul Brabban · Permalink · Leave a comment
In: Machine Learning, MSc